Use this when revising the main paper. EDBT papers are read by database-systems reviewers and published open access on OpenProceedings, so they need a data-management contribution stated on the first page and an evaluation a skeptic trusts. The failure this skill prevents is a technically fine paper that reads like a vague performance claim or a re-skinned ML result with a database title.
acmart or IEEEtran carried from a US venue.Regular / applied-systems paper
| Section | Job it must do | Common failure |
|---|---|---|
| Intro | Problem, inadequacy, contribution, evidence preview, what it enables — first page | Leads with "data is growing," not a concrete problem |
| Background / Motivation | Why current systems fall short, grounded | Motivation by assertion |
| Approach / System | The mechanism + integration, reproducibly | Described too thinly to reimplement |
| Evaluation | Each claim answered on real workloads vs. tuned baselines | Unnamed dataset, untuned baseline, toy scale |
| Scope / Discussion | Where it helps, is neutral, or costs; quantified | Limits hidden or hand-waved |
| Related work | Delta-first positioning against DB venues | Citation catalog with no contrast |
Experiments & Analysis paper — the study is the contribution: foreground the methodology (subjects, metrics, fairness, coverage, repeatability) and treat the findings as the deliverable, not a new system.
Vision paper — a tight argument for a new direction: the problem, why now, the sketch of an approach, and the research agenda, within the short budget; no need for a full evaluation, but the argument must be disciplined.
| Draft pattern | EDBT-safe rewrite |
|---|---|
| "Our system is significantly faster." | "reduces median query latency by X% (variance over N runs) vs. |
| "We evaluate on a large dataset." | "We evaluate on <named workload/logs>, sizes ..., cluster of ... workers; config in the artifact" |
| "Results show our approach works well." | "Under skew, straggler time drops by ...; on uniform keys, overhead is ...% (Table 2)" |
| "State-of-the-art performance." | Claim scoped to the workloads, metrics, and scale actually tested |
| "Our method scales." | "throughput holds from 8 to 128 workers (Fig. 2); beyond that is untested" |
[Where it helps] name the regime (e.g. concentrated, detectable skew) and show it
[Where it is neutral] show the no-op case (e.g. uniform keys) so the reviewer trusts you
[What it costs] quantify overhead / memory / setup, not "negligible"
[Where it fails] state the boundary (e.g. very short queries) and measure it
-> put each of these next to the affected result, not in a deferred paragraph
A draft with a sprawling background, five contributions, and nine plots: keep the two contributions the evaluation actually supports, the plots that carry the headline result and the overhead case, and a tight scope subsection; move parameter sweeps and secondary configurations to the artifact with explicit forward references. The test of a good cut: a reviewer should be able to answer "what does it do, on what workloads, versus what baseline, and what does it cost?" from the body alone.
[Writing diagnosis] clear / under-motivated / over-claimed / evidence-mismatched / over-scoped
[Shape] Regular / Experiments-&-Analysis / Vision — budget respected?
[First-page fix] <new framing leading with the data-management contribution>
[Claim audit] <claim -> workload/baseline/scale -> where answered -> proportional? yes/no>
[Scope fix] <where-it-helps / neutral / cost / failure, placed by the result>
[Template] host/OpenProceedings template in use? yes/no